Pith Number

pith:GKL2QZRM

pith:2025:GKL2QZRMLURGJ6EMJQYNNNHQ5V

not attested not anchored not stored refs resolved

Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps

Benedikt Mersch, Cyrill Stachniss, Meher V. R. Malladi, Niklas Trekel, Saurabh Gupta, Tiziano Guadagnino

A loop closure pipeline aligns LiDAR maps to ground then matches ORB features on density-preserving bird's-eye views to detect places across different sensors.

arxiv:2501.07399 v2 · 2025-01-13 · cs.RO

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{GKL2QZRMLURGJ6EMJQYNNNHQ5V}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The method handles various LiDAR sensors with different scanning patterns, fields of view, and resolutions. It generates local maps from LiDAR scans and aligns them using a ground alignment module to handle both planar and non-planar motion, uses density-preserving bird's-eye-view projections, extracts ORB feature descriptors, stores them in a binary search tree, and applies self-similarity pruning for accurate loop closure detection across platforms.

C2weakest assumption

That the combination of ground alignment and density-preserving BEV projections preserves sufficient place-specific geometric information for ORB features to enable reliable matching, even when sensor resolution, field of view, and motion profiles differ substantially between platforms.

C3one line summary

Introduces a sensor-agnostic loop closure pipeline for LiDAR SLAM using density maps, ground alignment, ORB on BEV projections, BST retrieval, and pruning to handle perceptual aliasing.

References

71 extracted · 71 resolved · 1 Pith anchors

[1] R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition 2016

[2] P. Besl and N. McKay. A Method for Registration of 3D Shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , 14(2):239–256, 1992 1992

[3] M. Bosse, P. Newman, J. Leonard, and S. Teller. Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework. Intl. Journal of Robotics Research (IJRR) , 23( 2004

[4] M. Bosse and R. Zlot. Place Recognition Using Keypoint V oting in Large 3D Lidar Datasets. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2013 2013

[5] N. Carlevaris-Bianco, A. Ushani, and R. Eustice. University of Michigan North Campus Long-term Vision and LiDAR Dataset. Intl. Journal of Robotics Research (IJRR) , 35(9):1023–1035, 2016 2016

Receipt and verification

First computed	2026-07-16T01:21:38.739213Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

3297a8662c5d2264f88c4c30d6b4f0ed54c06b5fb3a8af28208f627128f4be1b

Aliases

arxiv: 2501.07399 · arxiv_version: 2501.07399v2 · doi: 10.48550/arxiv.2501.07399 · pith_short_12: GKL2QZRMLURG · pith_short_16: GKL2QZRMLURGJ6EM · pith_short_8: GKL2QZRM

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GKL2QZRMLURGJ6EMJQYNNNHQ5V \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3297a8662c5d2264f88c4c30d6b4f0ed54c06b5fb3a8af28208f627128f4be1b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b2c41b13bc495332ee1d0405d600ac5e4838b697a245592d74e02a2dc658b164",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2025-01-13T15:17:10Z",
    "title_canon_sha256": "94f43c8c19658dbcf4c98ed9463d3628c2ef98076d403b6d844d71aad13c5f40"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2501.07399",
    "kind": "arxiv",
    "version": 2
  }
}